How to use tensorboard Embedding Projector? [closed]

Question

How do I use the Embedding Projector included in Tensorboard?

I can't find any documentation for it. There are some references to it here, but there's no step-by-step example/tutorial on how to use it.

Answer 1

As far as I am aware this is the only documentation about embedding visualization on the TensorFlow website. Though the code snippet might not be very instructive for the first time users, so here is an example usage:

import os import tensorflow as tf from tensorflow.examples.tutorials.mnist import input_data  LOG_DIR = 'logs'  mnist = input_data.read_data_sets('MNIST_data') images = tf.Variable(mnist.test.images, name='images')  with tf.Session() as sess:     saver = tf.train.Saver([images])      sess.run(images.initializer)     saver.save(sess, os.path.join(LOG_DIR, 'images.ckpt')) 

Here first we create a TensoFlow variable (images) and then save it using tf.train.Saver. After executing the code we can launch TensorBoard by issuing tensorboard --logdir=logs command and opening localhost:6006 in a browser.

However this visualisation is not very helpful because we do not see different classes to which each data point belongs. In order to distinguish each class from another one should provider some metadata:

import os import tensorflow as tf from tensorflow.examples.tutorials.mnist import input_data from tensorflow.contrib.tensorboard.plugins import projector   LOG_DIR = 'logs' metadata = os.path.join(LOG_DIR, 'metadata.tsv')  mnist = input_data.read_data_sets('MNIST_data') images = tf.Variable(mnist.test.images, name='images')  with open(metadata, 'w') as metadata_file:     for row in mnist.test.labels:         metadata_file.write('%d\n' % row)  with tf.Session() as sess:     saver = tf.train.Saver([images])      sess.run(images.initializer)     saver.save(sess, os.path.join(LOG_DIR, 'images.ckpt'))      config = projector.ProjectorConfig()     # One can add multiple embeddings.     embedding = config.embeddings.add()     embedding.tensor_name = images.name     # Link this tensor to its metadata file (e.g. labels).     embedding.metadata_path = metadata     # Saves a config file that TensorBoard will read during startup.     projector.visualize_embeddings(tf.summary.FileWriter(LOG_DIR), config) 

Which gives us:

Answer 2

Sadly, I cannot find a more comprehensive documentation. Below I collect all related resources:

• How-to : https://github.com/efeiefei/tensorflow_documents_zh/blob/master/get_started/embedding_viz.md
• Google Research Blog: announcement and animation
• Paper : https://arxiv.org/pdf/1611.05469v1.pdf
• Source : https://github.com/tensorflow/embedding-projector-standalone
• 2017 TF Dev Summit tutorial and code
• Issue #6322 has some pointers and examples

PS: Thanks for upvoting me. Now I can post all the links.

Update 2019-08

Now you can use Embedding Projector easily in Colab with PyTorch's SummaryWriter

import numpy as np import tensorflow as tf import tensorboard as tb tf.io.gfile = tb.compat.tensorflow_stub.io.gfile from torch.utils.tensorboard import SummaryWriter  vectors = np.array([[0,0,1], [0,1,0], [1,0,0], [1,1,1]]) metadata = ['001', '010', '100', '111']  # labels writer = SummaryWriter() writer.add_embedding(vectors, metadata) writer.close() %load_ext tensorboard %tensorboard --logdir=runs 

Update 2020-02

The %tensorboard magic now works properly again.